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See detailContributions to decision tree induction: bias/variance tradeoff and time series classification
Geurts, Pierre ULg

Doctoral thesis (2002)

Because of the rapid progress of computer and information technology, large amounts of data are nowadays available in a lot of domains. Automatic learning aims at developing algorithms able to produce ... [more ▼]

Because of the rapid progress of computer and information technology, large amounts of data are nowadays available in a lot of domains. Automatic learning aims at developing algorithms able to produce synthetic high-level information, or models, from this data. Learning algorithms are generally evaluated according to three different criteria: interpretability (how well the model helps to understand the data), predictive accuracy (how well the model can predict unseen situations), and computational efficiency (how fast is the algorithm and how it scales to large databases). This thesis explores two issues in automatic learning: the improvement of the well-known decision tree induction method and the problem of learning classification models for time series data. Decision tree induction method is an automatic learning algorithm which focuses on the modeling of input/output relationships. While this algorithm is among the fastest and most interpretable methods, its accuracy is not always competitive with respect to other algorithms. It is commonly admitted that this suboptimality is due to the excessive variance of this method. We first carry out an empirical study which shows quantitatively how important this variance is, i.e. how strongly decision trees depend on the random nature of the database used to infer them. These experiments confirm that this variance is detrimental not only from the point of view of accuracy but also from the point of view of interpretability. With the goal of improving both interpretability and accuracy, we consider three variance reduction techniques for decision trees. First, in the goal of improving mainly interpretability, we propose several methods which try to stabilize the parameters chosen during tree induction. While these methods succeed in reducing the variability of the parameters, they produce only a slight improvement of the accuracy. Then, we consider perturb and combine algorithms (e.g. bagging, boosting) which consist in combining the predictions of several models obtained by randomizing in some way the learning process. Inspired by the high variance of the parameters defining a decision tree, we propose an extremely randomized decision tree induction algorithm, called extra-tree, which chooses all parameters at random during induction. The aggregation of several of these extra-trees gives an important reduction of variance and this algorithm compares favorably in terms of accuracy and computational efficiency with both bagging and boosting. Because of the randomization of the parameters, the resulting method is also competitive with classical decision tree induction in terms of computational efficiency. In addition to these two approaches, we propose a ``dual'' perturb and combine algorithm which delays the perturbation at the prediction stage and hence requires only one model. In combination with decision tree, this method actually bridges the gap between single decision trees and perturb and combine algorithms. Of the first, it saves the interpretability (by using only one model), and with perturb and combine algorithm, it shares some of the accuracy (by reducing the variance). The second topic of the thesis is the problem of time series classification. The most direct way to solve this problem is to apply existing learning algorithms on low-level variables which correspond to the values of a time series at several time points. Experiments with the tree-based algorithms studied in the first part of the thesis shows that this approach is limited. A variance reduction techniques is then proposed specifically for this kind of data which consists in aggregating the prediction given by a classification model for subsequences of time series. Since this method does not provide interpretable models, we propose a second method which extends decision tree tests by allowing them to detect local shift invariant properties, or patterns, in time series. The study proposed in this part of the thesis is only a first step in the domain but our conclusions give some future work directions for handling complex type of data with automatic learning methods. [less ▲]

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See detailContributions to early land plant research (XIV IBC).
Gerrienne, Philippe ULg; Berry, Christopher M.

in Review of Palaeobotany and Palynology (2001), 116

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See detailContributions to early land plant research.
Gerrienne, Philippe ULg; Berry, Christopher M.

Book (2001)

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See detailContributions to Monte Carlo Search
Lupien St-Pierre, David ULg

Doctoral thesis (2013)

This research is motivated by improving decision making under uncertainty and in particular for games and symbolic regression. The present dissertation gathers research contributions in the field of Monte ... [more ▼]

This research is motivated by improving decision making under uncertainty and in particular for games and symbolic regression. The present dissertation gathers research contributions in the field of Monte Carlo Search. These contributions are focused around the selection, the simulation and the recommendation policies. Moreover, we develop a methodology to automatically generate an MCS algorithm for a given problem. For the selection policy, in most of the bandit literature, it is assumed that there is no structure or similarities between arms. Thus each arm is independent from one another. In several instances however, arms can be closely related. We show both theoretically and empirically, that a significant improvement over the state-of-the-art selection policies is possible. For the contribution on simulation policy, we focus on the symbolic regression problem and ponder on how to consistently generate different expressions by changing the probability to draw each symbol. We formalize the situation into an optimization problem and try different approaches. We show a clear improvement in the sampling process for any length. We further test the best approach by embedding it into a MCS algorithm and it still shows an improvement. For the contribution on recommendation policy, we study the most common in combination with selection policies. A good recommendation policy is a policy that works well with a given selection policy. We show that there is a trend that seems to favor a robust recommendation policy over a riskier one. We also present a contribution where we automatically generate several MCS algorithms from a list of core components upon which most MCS algorithms are built upon and compare them to generic algorithms. The results show that it often enables discovering new variants of MCS that significantly outperform generic MCS algorithms. [less ▲]

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See detailContributions to Recognizability: Self-generating Sets, Decidability, Automaticity and Multidimensional Sets
Lacroix, Anne ULg

Doctoral thesis (2013)

In this thesis, we study and answer several questions concerning recognizability of integer sets by finite automata. Each particular problem is the focus of a chapter. First, we study the recognizability ... [more ▼]

In this thesis, we study and answer several questions concerning recognizability of integer sets by finite automata. Each particular problem is the focus of a chapter. First, we study the recognizability of the so-called self-generating sets, initially introduced by C. Kimberling. In the second part, we study the syntactic complexity of any ultimately periodic set and we use our results to give an alternative decision procedure for a well-known decidability problem. Next, we give bounds on the automaticity of three different languages: the language of primitive words over a finite alphabet, the language of unbordered words over a finite alphabet and the language of representations of monic irreducible polynomials over a finite fields. Finally, we characterize the multidimensional sets that are recognizable in all abstract numeration systems. [less ▲]

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See detailContributions to the development of a test-day model adjusted to New Zealand dairy cattle
Vanderick, Sylvie ULg

Master's dissertation (2004)

A test-day model was developed in order to study the feasibility and the usefulness of a random regression test-day model based on dairy cattle data from New Zealand. (Co)variance components were ... [more ▼]

A test-day model was developed in order to study the feasibility and the usefulness of a random regression test-day model based on dairy cattle data from New Zealand. (Co)variance components were estimated first within breeds then across breeds for Holstein-Friesian and Jersey animals. Results from single breed analysis showed that there are several differences between the two main breeds of dairy cattle in New Zealand. This was confirmed with the genetic correlations below one (r²= 0.926 for milk in first lactation) for breed specific additive genetic effects provided by the across breeds analysis. The specificity of the model used was first the capacity for taking account of these differences between breeds since more than one third of the dairy population are crossbred; and secondly the estimation of different breeding values considering each animal as a potential mate within each of the two breeds. From genetic evaluations, ranking changes in sires were observed by using the model with breed specific additive genetic effects and a model assuming the same breed composition for all animals. This first investigation showed the opportunity to carry on more research in the way to develop an accurate test-day model permitting to evaluate the crossbred dairy cattle of New Zealand and to perform an optimal selection of animals in function of the mating strategy to apply. [less ▲]

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See detailContributions to the history of the germinal vesicle, and of the first embryonic nucleus
Van Beneden, Édouard ULg

in Quarterly Journal of Microscopical Science (The) (1876), 2(16), 153-182

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See detailContributions to the knowledge about the seismic behaviour of typical Belgian masonry houses
Degée, Hervé ULg; Plumier, André ULg

in Proceedings of the 8th International Masonry Conference (2010)

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See detailContributions to the modelling of fires in buildings and their effects on structures
Franssen, Jean-Marc ULg

Scientific conference (1998, July 24)

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See detailContributions to the process modelling of resin infusion under flexible tooling (RIFT) manufacturing for composite aerostructures
Pierce, R.S.; Falzon, B.G.; Thompson, M.C. et al

in Proceedings of ICCM19 (2013, July)

The cost of manufacturing textile-reinforced composite aerostructures using Resin Infusion under Flexible Tooling (RIFT) can be reduced by computational modelling. This paper outlines the current progress ... [more ▼]

The cost of manufacturing textile-reinforced composite aerostructures using Resin Infusion under Flexible Tooling (RIFT) can be reduced by computational modelling. This paper outlines the current progress and contributions made towards this goal. A continuum-based material model was incorporated into a finite element package to simulate the draping of a dry carbon fibre fabric. It accurately tracks the changes in the warp and weft fibre orientations and has been experimentally validated. Material characterisation was performed to determine the tensile and shear properties of a plain weave fabric reinforcement material. In support of bias extension shear testing, an accessible Digital Image Correlation (DIC) approach was developed for accurate optical strain measurement. A relationship between permeability and shear angle was also experimentally determined using a novel permeability measurement technique. Future work is planned to combine all these aspects in an infusion model and demonstrate the complete process model. [less ▲]

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See detailContributions to transnational flood risk management in the Meuse basin
Dewals, Benjamin ULg; Erpicum, Sébastien ULg; Archambeau, Pierre ULg et al

in European Symposium on Flood Frequency Estimation and Implications for Risk Management (2014, March)

Experience in transboundary river basins emphasizes the need for more cooperative transnational water management. In this respect, the Interreg IVB project AMICE aims at developing a shared adaptation ... [more ▼]

Experience in transboundary river basins emphasizes the need for more cooperative transnational water management. In this respect, the Interreg IVB project AMICE aims at developing a shared adaptation strategy of the basin of the river Meuse to the hydrological impacts of climate change. The basin covers parts of France, Belgium, Germany and the Netherlands. The project combines scientific research with innovative natural and structural water retention measures, as well as enhanced transnational crisis management. In this framework, common climate and hydrological scenarios were derived for the time horizons 2021-2050 and 2071-2100. A “coordinated” procedure was set up to conduct the first hydraulic modelling of the entire river Meuse, from its spring to its mouth. For the considered climate scenarios, the increases in future flood levels were found about twice higher in the central part of the Meuse basin compared to the upper and lower parts. This distinctive spatial pattern results from the morphology of the valley. The characteristics of the hydraulic models used for this “coordinated” analysis differed significantly from one region to the other (e.g., 1D vs. 2D, steady vs. unsteady). Therefore, a more refined analysis was undertaken on a 100 km long stretch of the Meuse, crossing the border between Belgium and the Netherlands, using very similar (“harmonized”) models for the whole computation (2D unsteady models). As a result, the influence of using different interconnected models could be appreciated. The results of hydraulic modelling were used to conduct damage assessment based on a common methodology throughout the Meuse basin and combined with landuse change scenarios in the floodplains (change in vulnerability of the floodplains). In turn, several adaptation measures were tested, including upstream water retention, dredging as well as upgrade of flood defences and mobile weirs. Hydrological and hydraulic modelling was also performed for a sub-basin of the river Meuse, partly controlled by two large reservoirs. A detailed model of the operation rules of these reservoirs was implemented. This enabled to investigate the impact of the considered climate change scenarios on the performance of these multi-purpose reservoirs as well as to investigate possible enhancements in the operation rules, which may serve as adaptation measures to compensate for the detrimental effects of climate change. [less ▲]

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See detailIl contributo della ricerca: i primi dati e le prospettive per la pratica
De Palo, Francesca; Sala, Alessia; Miscioscia, Marina ULg

Conference (2011, February 25)

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See detailContributor to Small Animal Ophthalmology, 4th Edition - A Problem-Oriented Approach (DVD)
Monclin, Sébastien ULg

in R.L. Peiffer; S.M. Petersen-Jones (Eds.) Small Animal Ophthalmology, 4th Edition - A Problem-Oriented Approach (2009)

Detailed reference viewed: 14 (1 ULg)
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See detailContributor to the Wonca Dictionary of General/Family Practice
Jamoulle, Marc ULg

in Bentzen, Niels (Ed.) Wonca Dictionary of General/Family Practice (2003)

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See detailContro la pastasciutta : Marinetti’s Futurist Lunch
Delville, Michel ULg

in Intervalles (2007), 2

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See detailControl and simulation of doubly fed induction generator for variable speed wind turbine systems based on an integrated finite element approach
Chen, Qiongzhong ULg; Defourny, Michel; Bruls, Olivier ULg

in Proceedings of the European Wind Energy Conference and Exhibition 2011 (EWEA 2011) (2011, March)

Regarding renewable energy and environment- friendly issues, wind energy nowadays has become the fastest-growing energy source in the world, and thus attracts a lot of research interest in the wind ... [more ▼]

Regarding renewable energy and environment- friendly issues, wind energy nowadays has become the fastest-growing energy source in the world, and thus attracts a lot of research interest in the wind turbine generation system. A doubly fed induction generator (DFIG) is used for variable speed operation in a wind turbine system to extract more power. Following a systematic approach, this paper investigates on the modelling and simulation of wind turbine generating systems using the flexible multibody simulation software SAMCEF/MECANO [1]. The objective of this work is to analyze the control-generator-structure interactions in a wind turbine system. Firstly, an extension of the finite element method is integrated into the flexible multibody dynamics solver, and thus extends the solver to represent mechatronic systems in a strongly-coupled way. Secondly, DFIG and the control systems are modularly modeled for the wind turbine package. Control of DFIG for grid synchronization and power optimization are elaborated in detail, and the methods are validated through a 2MW DFIG wind turbine prototype model. At the end, a systematic system model of wind turbine structure connected with the DFIG generating system is presented, which provides the dynamic analysis for the whole system in an overall range. [less ▲]

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See detailControl and Voting Power in Complex Shareholding Networks
Crama, Yves ULg; Leruth, Luc ULg; Su Wang

Conference (2011, June)

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